Title

Seagrass Resource Assessment Using Remote Sensing Methods in St. Joseph Sound and Clearwater Harbor, Florida, USA

Document Type

Article

Publication Date

2-2012

Keywords

Landsat TM, maximum likelihood, Mahalanobis distance, Gulf of Mexico, submerged aquatic, vegetation

Digital Object Identifier (DOI)

http://dx.doi.org/10.1007/s10661-011-2028-4

Abstract

In the event of a natural or anthropogenic disturbance, environmental resource managers require a reliable tool to quickly assess the spatial extent of potential damage to the seagrass resource. The temporal availability of the Landsat 5 Thematic Mapper (TM) imagery provided a suitable option to detect and assess damage of the submerged aquatic vegetation (SAV). This study examined Landsat TM imagery classification techniques to create two-class (SAV presence/absence) and three-class (SAV estimated coverage) SAV maps of the seagrass resource. The Mahalanobis Distance method achieved the highest overall accuracy (86%) and validation accuracy (68%) for delineating the seagrass resource (two-class SAV map). The Maximum Likelihood method achieved the highest overall accuracy (74%) and validation accuracy (70%) for delineating the seagrass resource three-class SAV map. The Landsat 5 TM imagery classification provided a seagrass resource map product with similar accuracy to the aerial photointerpretation maps (validation accuracy 71%). The results support the application of remote sensing methods to analyze the spatial extent of the seagrass resource.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

Environmental Monitoring and Assessment, v. 184, issue 2, p. 1131-1143

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